Bibliographic record
Abstract
Concrete structures are becoming more and more sophisticated and submitted to severe conditions, for example: high stresses and temperatures, cyclic loadings, earthquakes, etc. It is therefore necessary to simulate correctly the behaviour and damage of such structures. However, the behaviour of this material is among the most complex ones: various phenomena are observed experimentally, such as, loss of stiffness, irreversible strains, stiffness recovery and dissymmetric behaviour to mention a few. If all these effects are taken into consideration, it would lead to models that use numerous parameters. In this paper, a framework for damage mechanics of concrete is presented and used to simulate the nonlinear behaviour of concrete using finite element method (FEM). A relatively simple isotropic damage model, containing essentially no adjustable parameters is shown to produce results in remarkably good agreement with experimental results. Indeed, the damage law requires only the fracture energy to be completely defined. A special form of damage surfaces is constructed to illustrate the application of the model. A new damage criterion, defined as an equivalent strain norm, is proposed to take into consideration the dissymmetric behaviour of concrete. To verify the FEM program including the model, the predicted deformations are compared with experimental results and results from other nonlinear constitutive models.Key words: elasticity, quasi-brittle materials, damage, cracking, nonlinear behaviour, concrete modelling, finite element, simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".